Displaying publications 101 - 120 of 987 in total

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  1. A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images
    Letchumanan N, Wong JHD, Tan LK, Ab Mumin N, Ng WL, Chan WY, et al.
    J Digit Imaging, 2023 Aug;36(4):1533-1540.
    PMID: 37253893 DOI: 10.1007/s10278-022-00753-1
    This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision.
    Matched MeSH terms: Machine Learning
  2. "Tell me what is 'better'!" How medical students experience feedback, through the lens of self-regulatory learning
    Spooner M, Larkin J, Liew SC, Jaafar MH, McConkey S, Pawlikowska T
    BMC Med Educ, 2023 Nov 22;23(1):895.
    PMID: 37993832 DOI: 10.1186/s12909-023-04842-9
    INTRODUCTION: While feedback aims to support learning, students frequently struggle to use it. In studying feedback responses there is a gap in explaining them in relation to learning theory. This study explores how feedback experiences influence medical students' self-regulation of learning.

    METHODS: Final-year medical students across three campuses (Ireland, Bahrain and Malaysia) were invited to share experiences of feedback in individual semi-structured interviews. The data were thematically analysed and explored through the lens of self-regulatory learning theory (SRL).

    RESULTS: Feedback interacts with learners' knowledge and beliefs about themselves and about learning. They use feedback to change both their cognitive and behavioural learning strategies, but how they choose which feedback to implement is complex. They struggle to generate learning strategies and expect teachers to make sense of the "how" in addition to the "what"" in planning future learning. Even when not actioned, learners spend time with feedback and it influences future learning.

    CONCLUSION: By exploring our findings through the lens of self-regulation learning, we advance conceptual understanding of feedback responses. Learners' ability to generate "next steps" may be overestimated. When feedback causes negative emotions, energy is diverted from learning to processing distress. Perceived non-implementation of feedback should not be confused with ignoring it; feedback that is not actioned often impacts learning.

    Matched MeSH terms: Learning
  3. Smartic: A smart tool for Big Data analytics and IoT
    Sayeed S, Ahmad AF, Peng TC
    F1000Res, 2022;11:17.
    PMID: 38269303 DOI: 10.12688/f1000research.73613.1
    The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. We experimented with different missing value imputation techniques and compared machine learning (ML) model performances with different imputation methods. We propose a hybrid model for missing value imputation combining ML and sample-based statistical techniques. Furthermore, we continued with the best missing value inputted dataset, chosen based on ML model performance for feature engineering and hyperparameter tuning. We used k-means clustering and principal component analysis. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, we used K-fold cross-validation.
    Matched MeSH terms: Machine Learning
  4. Fulltext Effects of problem-based learning on EFL learning: A systematic review
    Guo Q, Jamil H, Ismail L, Luo S, Sun Z
    PLoS One, 2024;19(12):e0307819.
    PMID: 39666681 DOI: 10.1371/journal.pone.0307819
    Teaching English as a foreign language (EFL) is a priority globally, but pedagogical methods do not always keep up with the evolving needs of learners. Problem-based learning (PBL) is an innovative pedagogical approach that facilitates students' self-regulated learning, thereby improving their English proficiency. The present systematic literature review therefore concentrates on the application of PBL methodology in improving students' English language proficiency. It was conducted according to the systematic review and meta-analysis Preferred Reporting Items for Meta-Analyses (PRISMA) review methodology. In total, 27 articles related to PBL to improve English proficiency published between 2012 and 2023 were identified from Web of Science, Scopus, ProQuest, ERIC, and ScienceDirect databases. In the light of the findings, PBL has a positive effect on students' behaviour, academic performance, and critical thinking. Consequently, this paper contributes to policy makers, educators, and students to improve the English proficiency of students at all levels of education using PBL approach.
    Matched MeSH terms: Learning
  5. Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review
    Gangwal A, Ansari A, Ahmad I, Azad AK, Wan Sulaiman WMA
    Comput Biol Med, 2024 Sep;179:108734.
    PMID: 38964243 DOI: 10.1016/j.compbiomed.2024.108734
    Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.
    Matched MeSH terms: Machine Learning
  6. Fulltext Factors influencing consumer purchase decisions for health-promoting goods and services in malaysia
    Cheah YK
    Malays J Med Sci, 2014 Nov-Dec;21(6):36-44.
    PMID: 25897281 MyJurnal
    In the context of global increases in the prevalence of non-communicable diseases, the objective of the present study is to investigate the factors affecting individuals' decisions to use health-promoting goods and services.
    Matched MeSH terms: Learning
  7. Dental Students' Educational Environment and Perceived Stress: The University of Malaya Experience
    Myint K, See-Ziau H, Husain R, Ismail R
    Malays J Med Sci, 2016 May;23(3):49-56.
    PMID: 27418869
    An equitable and positive learning environment fosters deep self-directed learning in students and, consequently, good practice in their profession. Although demotivating weaknesses may lead to repeated day-to-day stress with a cascade of deleterious consequences at both personal and professional levels, a possible relationship between these parameters has not been reported. This study was undertaken to determine the relationship between students' perceptions of their educational environment and their stress levels.
    Matched MeSH terms: Learning
  8. Case Reports and Movement Disorder Rounds: Learning through observation, building on collective experiences
    Tan AH, Gatto EM
    Parkinsonism Relat Disord, 2023 May;110:105396.
    PMID: 37045676 DOI: 10.1016/j.parkreldis.2023.105396
    Matched MeSH terms: Learning
  9. Singing and learning physiology
    Cheng HM
    Med Teach, 2010 Jan;32(1):91-2.
    PMID: 20104662
    Matched MeSH terms: Learning*
  10. Addressing the learning needs of multidisciplinary students at a distance using a virtual learning environment (VLE): A novice teacher reflects
    Janes G
    Nurse Educ Pract, 2006 Mar;6(2):87-97.
    PMID: 19040861 DOI: 10.1016/j.nepr.2005.09.003
    This paper analyses the experience of one individual in the development and delivery of an innovative, undergraduate leadership development module. The module is accessed by practising health care professionals in Malaysia as part of a top-up Honours Degree and is delivered solely using a virtual learning environment (VLE), in this case Blackboard. The aim of this analysis is to contribute to the current body of knowledge regarding the use of VLE technology to facilitate learning at a distance. Of particular relevance is the paper's focus on: the drivers for e-learning; widening participation and increasing access; the experience of designing and delivering learning of relevance for this contemporary student population and evaluating the VLE experience/module. The development and delivery of this module is one result of a rapidly growing area of education. As a novice teacher in her first year in the higher education sector, this experience was a significant and stimulating challenge for a number of reasons and these are explored in greater depth. This is achieved by means of personal reflection using the phases of module development and delivery as a focus.
    Matched MeSH terms: Learning; Problem-Based Learning
  11. Fulltext The implementation of problem-based learning in collaborative groups in a chiropractic program in Malaysia
    Win NN, Nadarajah VD, Win DK
    J Educ Eval Health Prof, 2015;12:17.
    PMID: 25961676 DOI: 10.3352/jeehp.2015.12.17
    PURPOSE: Problem-based learning (PBL) is usually conducted in small-group learning sessions with approximately eight students per facilitator. In this study, we implemented a modified version of PBL involving collaborative groups in an undergraduate chiropractic program and assessed its pedagogical effectiveness.
    METHODS: This study was conducted at the International Medical University, Kuala Lumpur, Malaysia, and involved the 2012 chiropractic student cohort. Six PBL cases were provided to chiropractic students, consisting of three PBL cases for which learning resources were provided and another three PBL cases for which learning resources were not provided. Group discussions were not continuously supervised, since only one facilitator was present. The students' perceptions of PBL in collaborative groups were assessed with a questionnaire that was divided into three domains: motivation, cognitive skills, and perceived pressure to work.
    RESULTS: Thirty of the 31 students (97%) participated in the study. PBL in collaborative groups was significantly associated with positive responses regarding students' motivation, cognitive skills, and perceived pressure to work (P<0.05). The students felt that PBL with learning resources increased motivation and cognitive skills (P<0.001).
    CONCLUSION: The new PBL implementation described in this study does not require additional instructors or any additional funding. When implemented in a classroom setting, it has pedagogical benefits equivalent to those of small-group sessions. Our findings also suggest that students rely significantly on available learning resources.
    KEYWORDS: Chiropractic; Learning; Motivation; Perception; Problem-based learning
    Matched MeSH terms: Problem-Based Learning*
  12. Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification
    Poon HK, Yap WS, Tee YK, Lee WK, Goi BM
    Neural Netw, 2019 Nov;119:299-312.
    PMID: 31499354 DOI: 10.1016/j.neunet.2019.08.017
    Document classification aims to assign one or more classes to a document for ease of management by understanding the content of a document. Hierarchical attention network (HAN) has been showed effective to classify documents that are ambiguous. HAN parses information-intense documents into slices (i.e., words and sentences) such that each slice can be learned separately and in parallel before assigning the classes. However, introducing hierarchical attention approach leads to the redundancy of training parameters which is prone to overfitting. To mitigate the concern of overfitting, we propose a variant of hierarchical attention network using adversarial and virtual adversarial perturbations in 1) word representation, 2) sentence representation and 3) both word and sentence representations. The proposed variant is tested on eight publicly available datasets. The results show that the proposed variant outperforms the hierarchical attention network with and without using random perturbation. More importantly, the proposed variant achieves state-of-the-art performance on multiple benchmark datasets. Visualizations and analysis are provided to show that perturbation can effectively alleviate the overfitting issue and improve the performance of hierarchical attention network.
    Matched MeSH terms: Machine Learning*
  13. Fulltext DeepMoney: counterfeit money detection using generative adversarial networks
    Ali T, Jan S, Alkhodre A, Nauman M, Amin M, Siddiqui MS
    PeerJ Comput Sci, 2019;5:e216.
    PMID: 33816869 DOI: 10.7717/peerj-cs.216
    Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system-dubbed DeepMoney-is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.
    Matched MeSH terms: Machine Learning; Unsupervised Machine Learning
  14. Personality traits and learning
    Kohli S, Bhatia S
    Br Dent J, 2021 02;230(4):186.
    PMID: 33637900 DOI: 10.1038/s41415-021-2752-2
    Matched MeSH terms: Learning*
  15. Fulltext An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector Embedding
    Hoque MS, Jamil N, Amin N, Lam KY
    Sensors (Basel), 2021 Jun 20;21(12).
    PMID: 34202977 DOI: 10.3390/s21124220
    Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.
    Matched MeSH terms: Machine Learning*
  16. Fulltext Physiology without borders: report on physiology education workshops in India-IUPS Initiatives (2018-2019)
    Chandran DS, Muthukrishnan SP, Barman SM, Peltonen LM, Ghosh S, Sharma R, et al.
    Adv Physiol Educ, 2020 09 01;44(3):309-313.
    PMID: 32484399 DOI: 10.1152/advan.00050.2020
    Matched MeSH terms: Problem-Based Learning*
  17. Fulltext Ridge regression and its applications in genetic studies
    Arashi M, Roozbeh M, Hamzah NA, Gasparini M
    PLoS One, 2021;16(4):e0245376.
    PMID: 33831027 DOI: 10.1371/journal.pone.0245376
    With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings.
    Matched MeSH terms: Machine Learning*
  18. Fulltext Applying the learning theories to medical education: A commentary
    Sivalingam Nalliah, Nazimah Idris
    International e-Journal of Science, Medicine & Education, 2014;8(1):50-57.
    MyJurnal
    Medical education of today continues to evolve to meet the challenges of the stakeholders. Medical professionals today are expected to
    play multiple roles besides being experts. Thus, the curriculum has to be developed in a manner that facilitates learners to achieve the intended goal of becoming a medical professional with multiple competencies. The understanding of learning theories will be helpful in designing and delivering the curriculum to meet the demands of producing a medical professional who would meet the CanMEDS model.
    This commentary explores and reflects on the learning theories of behaviorism, cognitivism and constructivism as they have evolved over time and the application of these learning theories in medical education, particularly in the context of medical education in Malaysia. The authors are convinced that these three theories are not mutually exclusive but should be operationalized contextually and throughout the
    different stages of learning in the MBBS curriculum. Understanding these theories and their application will enhance the learning experience of students.
    Matched MeSH terms: Learning; Problem-Based Learning
  19. Fulltext Learning through teaching and sharing in the Jigsaw Classroom
    Ab Murat, N.
    Ann Dent, 2008;15(2):71-76.
    MyJurnal
    Teaching is a complex activity which consists not only of giving instructions but also promotion of learning. Different students have different preference for learning styles. Dental educators must therefore attempt to mix and match their methods of teaching to accommodate students with differing learning styles to provide an opportunity to maximize their learning. This paper aims to share the writer's experience and students' perceptions towards a different mode of teaching/learning method. The Jigsaw Classroom method was employed on University of Malaya's third-year dental students during their Water Fluoridation lecture. At the end of the session, students were asked to reflect upon the learning experience and to inscribe their feelings. Initially, students showed their resentment towards the new learning style but their resistance changed once they got into a group and started to learn from each other. In the reflective essay, most students expressed that learning through teaching and discussing as required in the Jigsaw method enhanced their understanding of the topic and they claimed that they were able to retain the information better. In this study, the Jigsaw method proved that learning in the lecture hall can be fun, educational and enriching.
    Matched MeSH terms: Learning; Problem-Based Learning
  20. Fulltext The naqli and ‘aqli integration in the teachings of community health posting
    Dzulhairi, M.R., Zarina, A.R., Nooriah, M.S., Yunus, M.
    International Medical Journal Malaysia, 2015;14(1):83-86.
    MyJurnal
    The Community Health Posting teaching module is incorporated in the fourth year medical curriculum at Universiti Sains Islam Malaysia (USIM). The integration of Islamic principles and values in the medical curriculum is emphasized during the Community Health Posting. The Community Health curriculum allow students to appreciate and understand the medical and fiqh aspects of health and disease, the social issues in medical practice and research and to inculcate the practice of Islamic professional etiquettes. The teaching module illustrates the relevance of humanities in understanding illness and medical care within the community. Teaching and learning activities include components that enable the students to explore a wide range of influencing factors and how these affect the patients and their families. Issues pertaining to psychosocial and ecological perspectives of the community are also discussed. This posting utilizes various teaching and learning techniques such as lectures, tutorials, seminars, group discussions, educational visits, practical sessions and patient bedside teaching. In addition, the students are equipped with Islamic knowledge through the integration of Naqli and Aqli components in the Community Health Posting curriculum.
    Matched MeSH terms: Learning; Problem-Based Learning
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